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On the laws of large numbers for nonnegative random variables

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  • Etemadi, Nasrollah

Abstract

Strong laws of large numbers concerning nonnegative random variables are obtained and then they are utilized to establish stability results, among other things, for sums of pairwise independent random variables and the range of random walks.

Suggested Citation

  • Etemadi, Nasrollah, 1983. "On the laws of large numbers for nonnegative random variables," Journal of Multivariate Analysis, Elsevier, vol. 13(1), pages 187-193, March.
    • Handle: RePEc:eee:jmvana:v:13:y:1983:i:1:p:187-193
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    Citations

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    Cited by:

    1. Tappe, Stefan, 2021. "A note on the von Weizsäcker theorem," Statistics & Probability Letters, Elsevier, vol. 168(C).
    2. Siemroth, Christoph, 2014. "Why prediction markets work : The role of information acquisition and endogenous weighting," Working Papers 14-02, University of Mannheim, Department of Economics.
    3. Soo Hak Sung, 2014. "Marcinkiewicz–Zygmund Type Strong Law of Large Numbers for Pairwise i.i.d. Random Variables," Journal of Theoretical Probability, Springer, vol. 27(1), pages 96-106, March.
    4. Narayanaswamy Balakrishnan & Alexei Stepanov, 2013. "Runs Based on Records: Their Distributional Properties and an Application to Testing for Dispersive Ordering," Methodology and Computing in Applied Probability, Springer, vol. 15(3), pages 583-594, September.
    5. Chen, Pingyan & Sung, Soo Hak, 2016. "A strong law of large numbers for nonnegative random variables and applications," Statistics & Probability Letters, Elsevier, vol. 118(C), pages 80-86.
    6. Wallsten, Thomas S. & Diederich, Adele, 2001. "Understanding pooled subjective probability estimates," Mathematical Social Sciences, Elsevier, vol. 41(1), pages 1-18, January.
    7. Lita da Silva, João, 2018. "Strong laws of large numbers for pairwise quadrant dependent random variables," Statistics & Probability Letters, Elsevier, vol. 137(C), pages 349-358.
    8. Alexander J. Bogensperger & Yann Fabel & Joachim Ferstl, 2022. "Accelerating Energy-Economic Simulation Models via Machine Learning-Based Emulation and Time Series Aggregation," Energies, MDPI, vol. 15(3), pages 1-42, February.
    9. Stepanov, A., 2011. "Limit theorems for runs based on 'small spacings'," Statistics & Probability Letters, Elsevier, vol. 81(1), pages 54-61, January.
    10. Quang, Nguyen Van & Son, Do The & Son, Le Hong, 2017. "The strong laws of large numbers for positive measurable operators and applications," Statistics & Probability Letters, Elsevier, vol. 124(C), pages 110-120.

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